Domain-specific feature engineering
Instruction and application
In ProgressDomain-specific feature engineering
Feature engineering is never fully one-size-fits-all. Different industries generate different signals, constraints and failure modes, so strong features often come from domain knowledge rather than generic preprocessing alone.
Financial data
Financial modelling often depends on temporal patterns, risk ratios and market indicators.
- Technical indicators such as moving averages, RSI and MACD
- Financial ratios such as debt-to-equity and return on assets
Health care data
Health care features often need to encode patient condition, history and risk in clinically meaningful ways.
- Comorbidity indices
- Readmission or complication risk scores
- Structured summaries of diagnosis and treatment history
Retail and e-commerce
Retail models benefit from behavioural features that summarise how customers buy and how recently they engaged.
- RFM features: Recency, Frequency, Monetary value
- Customer segment labels
- Promotion response and basket behaviour
IoT and sensor data
Sensor streams usually require signal-aware transformations rather than simple tabular cleaning.
- Rolling statistics and Fourier features
- Spectral or wavelet-based descriptors
- Anomaly-focused aggregates from high-frequency signals
Image data without deep learning
Traditional image pipelines still use domain-aware features such as colour histograms, edge detectors and texture descriptors when smaller datasets or faster workflows are needed.
Download the image example dataset
Core idea
The best features often come from understanding the operational meaning of the data. A useful finance feature may be meaningless in health care, and a strong health care signal may be impossible to interpret in retail.